Introduction

Estimated Course Time: 4 hours

Welcome to Recommendation Systems! We've designed this course
to expand your knowledge of recommendation systems and explain
different models used in recommendation, including matrix
factorization and deep neural networks.

Objectives:

Describe the purpose of recommendation systems.

Understand the components of a recommendation system including
candidate generation, scoring, and re-ranking.

Use embeddings to represent items and queries.

Develop a deeper technical understanding of common techniques
used in candidate generation.

Use TensorFlow to develop two models used for recommendation:
matrix factorization and softmax.